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100416s2010 gw | s |||| 0|eng d |
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|a 9783642128349
|9 978-3-642-12834-9
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|a 10.1007/978-3-642-12834-9
|2 doi
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|a TA329-348
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|a TA640-643
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|a TBJ
|2 bicssc
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|a MAT003000
|2 bisacsh
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|a 519
|2 23
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|a Chen, Ying-ping.
|e editor.
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|a Exploitation of Linkage Learning in Evolutionary Algorithms
|c edited by Ying-ping Chen.
|h [electronic resource] /
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| 264 |
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg,
|c 2010.
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| 300 |
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|a 265p. 30 illus. in color.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
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|a text file
|b PDF
|2 rda
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|a Evolutionary Learning and Optimization,
|v 3
|x 1867-4534 ;
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|a Part I Linkage & Problem Structures -- Part II Model Building & Exploiting -- Part III Applications.
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| 520 |
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|a One major branch of enhancing the performance of evolutionary algorithms is the exploitation of linkage learning. This monograph aims to capture the recent progress of linkage learning, by compiling a series of focused technical chapters to keep abreast of the developments and trends in the area of linkage. In evolutionary algorithms, linkage models the relation between decision variables with the genetic linkage observed in biological systems, and linkage learning connects computational optimization methodologies and natural evolution mechanisms. Exploitation of linkage learning can enable us to design better evolutionary algorithms as well as to potentially gain insight into biological systems. Linkage learning has the potential to become one of the dominant aspects of evolutionary algorithms; research in this area can potentially yield promising results in addressing the scalability issues.
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|a Engineering.
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|a Artificial intelligence.
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|a Mathematics.
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|a Engineering mathematics.
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|a Engineering.
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2 |
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|a Appl.Mathematics/Computational Methods of Engineering.
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| 650 |
2 |
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|a Artificial Intelligence (incl. Robotics).
|
| 650 |
2 |
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|a Applications of Mathematics.
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| 710 |
2 |
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|a SpringerLink (Online service)
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|t Springer eBooks
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| 776 |
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8 |
|i Printed edition:
|z 9783642128332
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| 830 |
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|a Evolutionary Learning and Optimization,
|v 3
|x 1867-4534 ;
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| 856 |
4 |
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|u https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-3-642-12834-9
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| 912 |
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|a ZDB-2-ENG
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| 950 |
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|a Engineering (Springer-11647)
|